What type of analysis can be performed with Datadog's Outlier Detection feature?

The Outlier Detection feature in Datadog is specifically designed to identify anomalous behavior within your metrics. This capability allows organizations to quickly pinpoint metrics that significantly deviate from the norm, which could indicate underlying issues or irregularities in performance.

The primary function of Outlier Detection is to leverage machine learning algorithms to analyze the behavior of metrics over time and compare individual metric instances to their historical patterns. This means that if a particular metric suddenly spikes or drops unexpectedly, the Outlier Detection can flag it as an anomaly. Detecting such outliers is crucial for maintaining the health of applications and infrastructure, as it helps users respond to potential problems proactively.

In contrast, other options such as tracking user engagement or analyzing historical trends focus on different aspects of data analysis that are not the primary goal of the Outlier Detection feature. Measuring network traffic between servers also serves a different purpose, which is monitoring rather than anomaly detection. Thus, the distinction lies in the targeted application of Outlier Detection within the broader scope of monitoring and analytics provided by Datadog.